---
title: "LLM Integration"
description: "Integrate any LLM with mcp_use through LangChain"
icon: "brain"
---

# LLM Integration Guide

mcp_use supports integration with **any** Language Learning Model (LLM) that is compatible with LangChain. This guide covers how to use different LLM providers with mcp_use and emphasizes the flexibility to use any LangChain-supported model.

<Note>
**Key Requirement**: Your chosen LLM must support **tool calling** (also known as function calling) to work with MCP tools. Most modern LLMs support this feature.
</Note>

## Universal LLM Support

mcp_use leverages LangChain's architecture to support any LLM that implements the LangChain interface. This means you can use virtually any model from any provider, including:

<CardGroup cols={2}>
  <Card title="OpenAI" icon="robot">
    GPT-4, GPT-4o, GPT-3.5 Turbo
  </Card>
  <Card title="Anthropic" icon="anthropic">
    Claude 3.5 Sonnet, Claude 3 Opus, Claude 3 Haiku
  </Card>
  <Card title="Google" icon="google">
    Gemini Pro, Gemini Flash, PaLM
  </Card>
  <Card title="Open Source" icon="code">
    Llama, Mistral, CodeLlama via various providers
  </Card>
</CardGroup>

## Popular Provider Examples

### OpenAI

```python
from langchain_openai import ChatOpenAI
from mcp_use import MCPAgent, MCPClient

# Initialize OpenAI model
llm = ChatOpenAI(
    model="gpt-4o",
    temperature=0.7,
    api_key="your-api-key"  # Or set OPENAI_API_KEY env var
)

# Create agent
agent = MCPAgent(llm=llm, client=client)
```

### Anthropic Claude

```python
from langchain_anthropic import ChatAnthropic
from mcp_use import MCPAgent, MCPClient

# Initialize Claude model
llm = ChatAnthropic(
    model="claude-3-5-sonnet-20241022",
    temperature=0.7,
    api_key="your-api-key"  # Or set ANTHROPIC_API_KEY env var
)

# Create agent
agent = MCPAgent(llm=llm, client=client)
```

### Google Gemini

```python
from langchain_google_genai import ChatGoogleGenerativeAI
from mcp_use import MCPAgent, MCPClient

# Initialize Gemini model
llm = ChatGoogleGenerativeAI(
    model="gemini-pro",
    temperature=0.7,
    google_api_key="your-api-key"  # Or set GOOGLE_API_KEY env var
)

# Create agent
agent = MCPAgent(llm=llm, client=client)
```

### Groq (Fast Inference)

```python
from langchain_groq import ChatGroq
from mcp_use import MCPAgent, MCPClient

# Initialize Groq model
llm = ChatGroq(
    model="llama-3.1-70b-versatile",
    temperature=0.7,
    api_key="your-api-key"  # Or set GROQ_API_KEY env var
)

# Create agent
agent = MCPAgent(llm=llm, client=client)
```

### Local Models with Ollama

```python
from langchain_ollama import ChatOllama
from mcp_use import MCPAgent, MCPClient

# Initialize local Ollama model
llm = ChatOllama(
    model="llama3.1:8b",
    base_url="http://localhost:11434",  # Default Ollama URL
    temperature=0.7
)

# Create agent
agent = MCPAgent(llm=llm, client=client)
```

## Model Requirements

### Tool Calling Support

For MCP tools to work properly, your chosen model **must support tool calling**. Most modern LLMs support this:

✅ **Supported Models:**
- OpenAI: GPT-4, GPT-4o, GPT-3.5 Turbo
- Anthropic: Claude 3+ series
- Google: Gemini Pro, Gemini Flash
- Groq: Llama 3.1, Mixtral models
- Most recent open-source models

❌ **Not Supported:**
- Basic completion models without tool calling
- Very old model versions
- Models without function calling capabilities

### Checking Tool Support

You can verify if a model supports tools:

```python
# Check if the model supports tool calling
if hasattr(llm, 'bind_tools') or hasattr(llm, 'with_tools'):
    print("✅ Model supports tool calling")
else:
    print("❌ Model may not support tool calling")
```

## Model Configuration Tips

### Temperature Settings

Different tasks benefit from different temperature settings:

```python
# For precise, deterministic tasks
llm = ChatOpenAI(model="gpt-4o", temperature=0.1)

# For creative tasks
llm = ChatOpenAI(model="gpt-4o", temperature=0.8)

# Balanced approach (recommended)
llm = ChatOpenAI(model="gpt-4o", temperature=0.7)
```

### Model-Specific Parameters

Each provider has unique parameters you can configure:

```python
# OpenAI with custom parameters
llm = ChatOpenAI(
    model="gpt-4o",
    temperature=0.7,
    max_tokens=4000,
    top_p=0.9,
    frequency_penalty=0.1,
    presence_penalty=0.1
)

# Anthropic with custom parameters
llm = ChatAnthropic(
    model="claude-3-5-sonnet-20241022",
    temperature=0.7,
    max_tokens=4000,
    top_p=0.9
)
```

## Cost Optimization

### Choosing Cost-Effective Models

Consider your use case when selecting models:

| Use Case | Recommended Models | Reason |
|----------|-------------------|--------|
| Development/Testing | GPT-3.5 Turbo, Claude Haiku | Lower cost, good performance |
| Production/Complex | GPT-4o, Claude Sonnet | Best performance |
| High Volume | Groq models | Fast inference, competitive pricing |
| Privacy/Local | Ollama models | No API costs, data stays local |

### Token Management

```python
# Set reasonable token limits
llm = ChatOpenAI(
    model="gpt-4o",
    max_tokens=2000,  # Limit response length
    temperature=0.7
)

# Monitor usage in your application
agent = MCPAgent(
    llm=llm,
    client=client,
    max_steps=10  # Limit agent steps to control costs
)
```

## Environment Setup

Always use environment variables for API keys:

```bash
# .env file
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_API_KEY=AI...
GROQ_API_KEY=gsk_...
```

```python
from dotenv import load_dotenv
load_dotenv()  # Load environment variables

# Now LangChain will automatically use the keys
llm = ChatOpenAI(model="gpt-4o")  # No need to pass api_key
```

## Advanced Integration

### Custom Model Wrappers

You can create custom wrappers for specialized models:

```python
from langchain_core.language_models import BaseChatModel
from langchain_core.messages import BaseMessage

class CustomModelWrapper(BaseChatModel):
    """Custom wrapper for your model"""

    def _generate(self, messages, stop=None, **kwargs):
        # Your custom model implementation
        pass

    def _llm_type(self):
        return "custom_model"

# Use with mcp_use
llm = CustomModelWrapper()
agent = MCPAgent(llm=llm, client=client)
```

### Model Switching

Switch between models dynamically:

```python
def get_model_for_task(task_type: str):
    if task_type == "complex_reasoning":
        return ChatOpenAI(model="gpt-4o", temperature=0.1)
    elif task_type == "creative":
        return ChatAnthropic(model="claude-3-5-sonnet-20241022", temperature=0.8)
    else:
        return ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7)

# Use different models for different tasks
llm = get_model_for_task("complex_reasoning")
agent = MCPAgent(llm=llm, client=client)
```

## Troubleshooting

### Common Issues

1. **"Model doesn't support tools"**: Ensure your model supports function calling
2. **API key errors**: Check environment variables and API key validity
3. **Rate limiting**: Implement retry logic or use different models
4. **Token limits**: Adjust max_tokens or use models with larger context windows

### Debug Model Behavior

<CodeGroup>
```python Python
# Enable verbose logging to see model interactions
agent = MCPAgent(
    llm=llm,
    client=client,
    verbose=True  # Shows detailed model interactions
)
```
</CodeGroup>

For more LLM providers and detailed integration examples, visit the [LangChain Chat Models documentation](https://python.langchain.com/docs/integrations/chat/).
